AI reduces machine calibration time more than 30 times

Written by: Tom Austin-Morgan | Published:

Together with Siemens, AI startup Bonsai claims to have successfully applied deep reinforcement learning AI on a real-world machine in a test environment for the first time. Using Bonsai's AI Platform, Siemens subject matter experts trained an AI model to auto-calibrate a Computer Numerical Control (CNC) machine more than 30 times faster than an expert human operator.

CNC machines have revolutionised manufacturing since their inception in the 1940s. However, the value that CNC machines provide global manufacturers is constrained by high maintenance costs. To achieve highest possible quality of production, CNC machines need to be recalibrated frequently, as even minor friction leads to errors that result in costly manufacturing imperfections. Manufacturers have to bring in specialist engineers to do the job, which can take hours. While machines are decommissioned for maintenance, downtime and service costs rise. Costs run especially high when unplanned errors arise outside the regular maintenance schedule.

“Our successful project with Siemens represents a huge milestone in industrial AI, demonstrating the powerful results that can be achieved by combining machine teaching and machine learning,” said Mark Hammond, CEO and co-founder of Bonsai. “The beauty of this approach is that it balances the best of human and machine intelligence. Applied across the whole industrial manufacturing sector, the implications are staggering.”

At the core of Bonsai’s platform is an innovative ‘Machine Teaching’ technique, which enables specialist engineers to train machines to efficiently perform complex tasks. Using a simple scripting language, they can design the ‘lessons’ and ‘rewards’ required to train each task. Bonsai's AI Engine supports a wide range of state-of-the-art deep reinforcement learning algorithms, along with the logic for choosing the best-fit algorithms and guiding the training. In this way, the engineers can leverage AI without themselves having to gain a deep understanding of machine learning.

To build this proof of concept, the team used the AI engine to build a predictive model that would calibrate the CNC machine. Each model produced by Bonsai is referred to as a BRAIN (Basic Recurrent Artificial Intelligence Network). The AI engine trains each BRAIN using cutting-edge deep reinforcement learning algorithms.

After six months proving the concept, including training of the algorithms in a simulation environment, CCAM tested a BRAIN’s ability to calibrate a Siemens CNC controlled machine. The most successful BRAINs calibrated a CNC machine more than 30 times faster than the human operators, while achieving precision of less than two microns.

“The results we achieved using Bonsai demonstrate that organisations can deploy the latest AI technologies in a noisy real-world system,” said Michal Skubacz, Siemens vice president and head of industry software at Siemens Motion Control. “The solution possible based on the proof-of-concept with Bonsai could augment and scale the work of our best operators. Instead of having operators carry out the same work repeatedly, they can focus on training the machines to perform better and more advanced tasks.”

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